{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize The Policy Schedule of PBA\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /usr/local/lib/python3.6/dist-packages (0.9.0)\n",
      "Requirement already satisfied: matplotlib>=1.4.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (3.0.3)\n",
      "Requirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.14.5)\n",
      "Requirement already satisfied: pandas>=0.15.2 in /usr/local/lib/python3.6/dist-packages (from seaborn) (0.23.0)\n",
      "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2.4.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (1.1.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2.8.0)\n",
      "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn) (2019.1)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib>=1.4.3->seaborn) (1.12.0)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn) (41.0.1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install seaborn\n",
    "\n",
    "import PIL\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from cycler import cycler\n",
    "\n",
    "import pba.augmentation_transforms_hp as augmentation_transforms_hp\n",
    "from pba.utils import parse_log_schedule\n",
    "from pba.data_utils import parse_policy\n",
    "\n",
    "# Ignore the divided by zero warning in probability calculation\n",
    "np.seterr(divide='ignore', invalid='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize CIFAR & SVHN policies.\n",
    "cifar_policy = (parse_log_schedule('schedules/rcifar10_16_wrn.txt', 200), 'cifar10_4000')\n",
    "svhn_policy = (parse_log_schedule('schedules/rsvhn_16_wrn.txt', 160), 'svhn_1000')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "policy, dset = cifar_policy\n",
    "\n",
    "# Initilize dictionaries\n",
    "probability_results  = {'Rotate':[], 'TranslateX':[], 'TranslateY':[], 'Brightness':[], \n",
    "                        'Color':[], 'Invert':[] , 'Sharpness':[], 'Posterize':[], \n",
    "                        'ShearX':[], 'Solarize':[], 'ShearY':[], 'Equalize':[],\n",
    "                        'AutoContrast':[], 'Cutout':[],'Contrast' :[]}\n",
    "magnitude_results = copy.deepcopy(probability_results)\n",
    "upper_probability  = copy.deepcopy(probability_results)\n",
    "upper_magnitude = copy.deepcopy(probability_results)\n",
    "bottom_probability = copy.deepcopy(probability_results)\n",
    "bottom_magnitude= copy.deepcopy(probability_results)\n",
    "\n",
    "def parse_policy_hyperparams(policy_hyperparams):\n",
    "    \"\"\"We have two sets of hparams for each operation, which we need to split up.\"\"\"\n",
    "    split = len(policy_hyperparams) // 2\n",
    "    policy = parse_policy(policy_hyperparams[:split], augmentation_transforms_hp)\n",
    "    policy.extend(parse_policy(policy_hyperparams[split:], augmentation_transforms_hp))\n",
    "    return policy\n",
    "\n",
    "def mean_hyperparams(policy):\n",
    "    \"\"\"Get mean value of two set of hparams in each operation, \n",
    "    both magnitude and probability.\"\"\"\n",
    "    for one_policy in policy:\n",
    "        parsed_policy = parse_policy_hyperparams(one_policy)\n",
    "        half_policy = len(parsed_policy) // 2\n",
    "        # Follow the policy order in augmentation_transforms_hp.py \n",
    "        # line 64: name, probability, level = xform\n",
    "        for i in parsed_policy[:half_policy]:\n",
    "            upper_probability[i[0]].append(i[1])\n",
    "            upper_magnitude[i[0]].append(i[2])\n",
    "        for i in parsed_policy[half_policy:]: \n",
    "            bottom_probability[i[0]].append(i[1])\n",
    "            bottom_magnitude[i[0]].append(i[2])\n",
    "\n",
    "    for key, value in upper_probability.items():\n",
    "        upper_array = np.array(upper_probability[key])\n",
    "        bottom_array = np.array(bottom_probability[key])\n",
    "        tmp_array = np.divide((upper_array+bottom_array),2)\n",
    "        tmp_array = np.round(tmp_array, decimals=4).tolist()\n",
    "        probability_results[key]=tmp_array\n",
    "\n",
    "    for key, value in upper_magnitude.items():\n",
    "        upper_array = np.array(upper_magnitude[key])\n",
    "        bottom_array = np.array(bottom_magnitude[key])\n",
    "        tmp_array = np.divide((upper_array+bottom_array),2)\n",
    "        tmp_array = np.round(tmp_array, decimals=4).tolist()\n",
    "        magnitude_results[key]=tmp_array\n",
    "    return probability_results, magnitude_results\n",
    "\n",
    "probability_results, magnitude_results = mean_hyperparams(policy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set unique color of every operation in the plot.\n",
    "sns.set()\n",
    "sns.set_palette(sns.color_palette(\"hls\", 20))\n",
    "\n",
    "ind_constant = len(magnitude_results[\"Rotate\"])\n",
    "ind = np.arange(ind_constant)\n",
    "\n",
    "# Get sum of each operation magnitude at every epochs. \n",
    "# [sum of epoch1, sum of epoch2, sum of epoch3...]\n",
    "def sum_results_array (index_to_sum, results_dict):\n",
    "    sums = np.zeros(len(next(iter(results_dict.values()))))\n",
    "    i = 0\n",
    "    for value in results_dict.values():\n",
    "        value = np.array(value)\n",
    "        sums = sums+value\n",
    "        if i==index_to_sum:\n",
    "            break\n",
    "        i=i+1\n",
    "    return sums\n",
    "\n",
    "# Plot each bar\n",
    "plt.rcParams[\"figure.figsize\"] = [30,10]\n",
    "Rotate_bars = plt.bar(ind, magnitude_results[\"Rotate\"])\n",
    "TranslateX_bars = plt.bar(ind, magnitude_results[\"TranslateX\"], \n",
    "                          bottom=sum_results_array(0,magnitude_results))\n",
    "TranslateY_bars = plt.bar(ind, magnitude_results[\"TranslateY\"], \n",
    "                          bottom=sum_results_array(1,magnitude_results))\n",
    "Brightness_bars = plt.bar(ind, magnitude_results[\"Brightness\"], \n",
    "                          bottom=sum_results_array(2,magnitude_results))\n",
    "Color_bars = plt.bar(ind, magnitude_results[\"Color\"], \n",
    "                     bottom=sum_results_array(3,magnitude_results))\n",
    "Invert_bars = plt.bar(ind, magnitude_results[\"Invert\"], \n",
    "                      bottom=sum_results_array(4,magnitude_results))\n",
    "Sharpness_bars = plt.bar(ind, magnitude_results[\"Sharpness\"], \n",
    "                         bottom=sum_results_array(5,magnitude_results))\n",
    "Posterize_bars = plt.bar(ind, magnitude_results[\"Posterize\"], \n",
    "                         bottom=sum_results_array(6,magnitude_results))\n",
    "ShearX_bars = plt.bar(ind, magnitude_results[\"ShearX\"], \n",
    "                      bottom=sum_results_array(7,magnitude_results))\n",
    "Solarize_bars = plt.bar(ind, magnitude_results[\"Solarize\"], \n",
    "                        bottom=sum_results_array(8,magnitude_results))\n",
    "ShearY_bars = plt.bar(ind, magnitude_results[\"ShearY\"], \n",
    "                      bottom=sum_results_array(9,magnitude_results))\n",
    "Equalize_bars = plt.bar(ind, magnitude_results[\"Equalize\"], \n",
    "                        bottom=sum_results_array(10,magnitude_results))\n",
    "AutoContrast_bars = plt.bar(ind, magnitude_results[\"AutoContrast\"], \n",
    "                            bottom=sum_results_array(11,magnitude_results))\n",
    "Cutout_bars = plt.bar(ind, magnitude_results[\"Cutout\"], \n",
    "                      bottom=sum_results_array(12,magnitude_results))\n",
    "Contrast_bars = plt.bar(ind, magnitude_results[\"Contrast\"], \n",
    "                        bottom=sum_results_array(13,magnitude_results))\n",
    "\n",
    "plt.ylabel('Sum(magnitude)')\n",
    "plt.xlabel('Epochs')\n",
    "plt.title('Magnitudes')\n",
    "plt.xlim([0, ind_constant])\n",
    "bars_tag = [\"Rotate\",\"TranslateX\",\"TranslateY\",\"Brightness\",\"Color\",\"Invert\",\n",
    "            \"Sharpness\",\"Posterize\",\"ShearX\",\"Solarize\",\"ShearY\",\"Equalize\",\n",
    "            \"AutoContrast\",\"Cutout\",\"Contrast\"]\n",
    "plt.legend(bars_tag, loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.savefig(\"magnitude.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get total epochs \n",
    "epochs = len(next(iter(probability_results.values())))\n",
    "\n",
    "# Sum of probability. \n",
    "#[sum of epoch1, sum of epoch2, sum of epoch3...]\n",
    "sum_results = sum_results_array(epochs, probability_results)\n",
    "\n",
    "# Get every operation's probability portion in that epoch\n",
    "Rotate_1=np.nan_to_num(np.divide(probability_results[\"Rotate\"], sum_results))\n",
    "TranslateX_1 = np.nan_to_num(np.divide(probability_results[\"TranslateX\"], sum_results))\n",
    "TranslateY_1 = np.nan_to_num(np.divide(probability_results[\"TranslateY\"], sum_results))\n",
    "Brightness_1 = np.nan_to_num(np.divide(probability_results[\"Brightness\"], sum_results))\n",
    "Color_1 = np.nan_to_num(np.divide(probability_results[\"Color\"], sum_results))\n",
    "Invert_1 = np.nan_to_num(np.divide(probability_results[\"Invert\"], sum_results))\n",
    "Sharpness_1 = np.nan_to_num(np.divide(probability_results[\"Sharpness\"], sum_results))\n",
    "Posterize_1 = np.nan_to_num(np.divide(probability_results[\"Posterize\"], sum_results))\n",
    "ShearX_1 = np.nan_to_num(np.divide(probability_results[\"ShearX\"], sum_results))\n",
    "Solarize_1 = np.nan_to_num(np.divide(probability_results[\"Solarize\"], sum_results))\n",
    "ShearY_1 = np.nan_to_num(np.divide(probability_results[\"ShearY\"], sum_results))\n",
    "Equalize_1 = np.nan_to_num(np.divide(probability_results[\"Equalize\"], sum_results))\n",
    "AutoContrast_1 = np.nan_to_num(np.divide(probability_results[\"AutoContrast\"], sum_results))\n",
    "Cutout_1 = np.nan_to_num(np.divide(probability_results[\"Cutout\"], sum_results))\n",
    "Contrast_1 = np.nan_to_num(np.divide(probability_results[\"Contrast\"], sum_results))\n",
    "\n",
    "# Plot\n",
    "plt.rcParams[\"figure.figsize\"] = [20,10]\n",
    "ind = np.arange(epochs)\n",
    "plt.stackplot(ind, Rotate_1, TranslateX_1, TranslateY_1,Brightness_1,Color_1,\n",
    "              Invert_1,Sharpness_1,Posterize_1,ShearX_1,Solarize_1,ShearY_1,\n",
    "              Equalize_1,AutoContrast_1,Cutout_1,Contrast_1,labels=bars_tag)\n",
    "plt.legend(bars_tag, loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.xlim([0, epochs])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('Probability')\n",
    "plt.xlabel('Epochs')\n",
    "plt.title('Probability')\n",
    "plt.savefig(\"probability.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
